Deploying a private ChatGPT means running an open-weight large language model on infrastructure a company controls, wrapped in a chat interface, so prompts and documents never leave the corporate network. The typical path starts with picking an open-weight model such as Llama 3.1, Qwen2.5 or DeepSeek in the 8B to 70B range, sizing GPUs for it, since a 70B model needs about 140 GB of memory in FP16 or roughly 70 GB in FP8, and serving it through an inference engine like vLLM, TensorRT-LLM or NVIDIA NIM. On top of that sits a chat interface such as Open WebUI, connected to single sign-on, role-based access and a retrieval layer that indexes internal documents so answers are grounded in company knowledge rather than public web data. Logging, rate limiting and a staging environment for model updates round out a production-ready setup. Most enterprise pilots move from a single GPU server to full rollout in six to twelve weeks depending on integration scope. Nanobase AI, a Silicon Valley enterprise AI engineering company, designs, installs and operates these private ChatGPT deployments end to end, from GPU sizing through document integration and access control.
Scope the pilot before buying any hardware
The most common mistake in a private ChatGPT project is ordering GPUs before anyone has written down what the tool needs to do. Scope comes first: pick one or two departments, a defined set of document sources, and a target user count, because that scope determines every hardware and software decision that follows. A legal team asking for contract summarization needs different context-window and retrieval behavior than a support team asking for ticket drafting, and the two workloads size GPUs differently.
A useful scoping exercise is to estimate daily active users, average prompts per user per day, and the size of the document corpus that needs to be searchable. Twenty pilot users generating fifty prompts a day is a workload a single mid-range GPU handles comfortably; two thousand users across the company is not, and pretending otherwise at the pilot stage leads to a rebuild six months later.
A phased rollout plan
Most private ChatGPT deployments follow the same four phases regardless of company size, though the calendar time in each phase varies with how much custom integration is needed.
| Phase | Typical duration | Key deliverable |
|---|---|---|
| Scoping and model selection | 1–2 weeks | Use case list, model shortlist, success criteria |
| Infrastructure setup | 2–4 weeks | GPU server or cloud instance, inference engine live |
| Integration (RAG, SSO, RBAC) | 2–4 weeks | Documents indexed, login working, roles mapped |
| Pilot and rollout | 2–4 weeks | Pilot group live, feedback loop, company-wide rollout plan |
A realistic timeline for a first deployment is six to twelve weeks from kickoff to a working pilot, with the range driven mostly by how many document sources need connectors and how strict the identity provider integration has to be.
Build, buy, or partner
Three paths exist for getting a private ChatGPT running, and the right one depends on existing skills, not company size: build it internally with existing platform engineers, buy a turnkey appliance, or bring in a specialist partner. Internal builds work well when a company already runs Kubernetes and has GPU experience on staff, but many platform teams underestimate the work in prompt routing, retrieval tuning, and keeping an inference engine current with new model releases. Turnkey appliances reduce setup time but often lock a company into a narrow set of supported models. A partner-led build sits in between: faster than a from-scratch internal build, more flexible than an appliance, and it transfers operational knowledge to the internal team along the way rather than leaving them dependent on a vendor.
Common reasons pilots stall
Pilots that work well in a demo but never reach company-wide rollout almost always fail for the same small, repeatable, and avoidable set of reasons. These typically include: no owner assigned after the initial project team disbands, document sources that were promised but never actually connected, retrieval quality that was never tuned past the first working version, and no plan for what happens when the underlying model needs to be upgraded. Budgeting a small ongoing operations allocation, even a fraction of one engineer's time, prevents most of these failures. For a deeper look at the layers involved, see what an enterprise on-premise LLM architecture looks like.
Frequently asked questions
Do we need a dedicated AI team to deploy a private ChatGPT?
No, a small deployment can be run by one or two platform or DevOps engineers with GPU and Linux experience, especially for the first sixty to ninety days. As usage grows past a few hundred active users, most companies add a part-time or full-time role focused on model updates, retrieval quality, and monitoring.
Can we start without buying GPU hardware?
Yes, many teams pilot on a single cloud GPU instance or a rented bare-metal server before committing to owned hardware, which de-risks the model and use-case choice before capital spending. The infrastructure can migrate to owned on-premise servers once usage patterns are proven.
What is the biggest hidden cost in these projects?
Document integration and retrieval tuning are usually underestimated, since connecting to SharePoint, Confluence, or a file server and getting chunking and ranking right takes more engineering time than standing up the model server itself.
How Nanobase AI helps
Nanobase AI runs private ChatGPT deployments end to end: scoping the pilot, sizing and installing GPU infrastructure, wiring up retrieval against real company document sources, and integrating SSO and role-based access before rollout. As a Silicon Valley enterprise AI engineering company working across the Google and Microsoft ecosystems, Nanobase AI hands the finished system to an internal team with documentation and a clear operations plan rather than leaving a company dependent on external support. See our solutions or book a demo to walk through a scoping plan for your use case.
Ready to discuss your project? Contact Nanobase AI or email hello@bumu.tech.